release updates (#194)

* Adds qdrant check for filter attributs

* Submodule update

Author: JWittmeyer

controller/embedding/connector.py

@@ -3,6 +3,7 @@
 
 from util import service_requests
 import requests
+from graphql import GraphQLError
 
 BASE_URI = os.getenv("EMBEDDING_SERVICE")
 NEURAL_SEARCH_BASE_URI = os.getenv("NEURAL_SEARCH")
@@ -44,15 +45,29 @@ def request_re_embed_records(
 
 def update_attribute_payloads_for_neural_search(
     project_id: str, embedding_id: str, record_ids: Optional[List[str]] = None
-) -> None:
+) -> bool:
     url = f"{NEURAL_SEARCH_BASE_URI}/update_attribute_payloads"
     data = {
         "project_id": project_id,
         "embedding_id": embedding_id,
     }
     if record_ids:
         data["record_ids"] = record_ids
-    service_requests.post_call_or_raise(url, data)
+    try:
+        service_requests.post_call_or_raise(url, data)
+        return True
+    except GraphQLError:
+        print("couldn't update attribute payloads for neural search", flush=True)
+        return False
+
+
+def collection_on_qdrant(project_id: str, embedding_id: str) -> bool:
+    url = f"{NEURAL_SEARCH_BASE_URI}/collection/exist"
+    data = {
+        "project_id": project_id,
+        "embedding_id": embedding_id,
+    }
+    return service_requests.get_call_or_raise(url, data)["exists"]
 
 
 def update_label_payloads_for_neural_search(

controller/embedding/manager.py

@@ -262,15 +262,17 @@ def __recreate_embedding(project_id: str, embedding_id: str) -> Embedding:
 
 def update_embedding_payload(
     project_id: str, embedding_id: str, filter_attributes: List[str]
-) -> None:
+) -> bool:
     notification.send_organization_update(
         project_id=project_id,
         message=f"upload_embedding_payload:{str(embedding_id)}:start",
     )
-    embedding.update_embedding_filter_attributes(
-        project_id, embedding_id, filter_attributes, with_commit=True
-    )
-    connector.update_attribute_payloads_for_neural_search(project_id, embedding_id)
+    if connector.update_attribute_payloads_for_neural_search(project_id, embedding_id):
+        embedding.update_embedding_filter_attributes(
+            project_id, embedding_id, filter_attributes, with_commit=True
+        )
+        return True
+    return False
 
 
 def update_label_payloads_for_neural_search(

graphql_api/mutation/embedding.py

@@ -99,11 +99,14 @@ def mutate(
     ):
         auth.check_demo_access(info)
         auth.check_project_access(info, project_id)
-        manager.update_embedding_payload(project_id, embedding_id, filter_attributes)
-        notification.send_organization_update(
-            project_id, f"embedding_updated:{embedding_id}"
+        went_through = manager.update_embedding_payload(
+            project_id, embedding_id, filter_attributes
         )
-        return UpdateEmbeddingPayload(ok=True)
+        if went_through:
+            notification.send_organization_update(
+                project_id, f"embedding_updated:{embedding_id}"
+            )
+        return UpdateEmbeddingPayload(ok=went_through)
 
 
 class EmbeddingMutation(graphene.ObjectType):

graphql_api/types.py

@@ -26,6 +26,7 @@
     resolve_inter_annotator_matrix_classification,
     resolve_inter_annotator_matrix_extraction,
 )
+from controller.embedding.connector import collection_on_qdrant
 
 
 class User(SQLAlchemyObjectType):
@@ -152,6 +153,7 @@ class Meta:
     dimension = graphene.Int()
     count = graphene.Int()
     progress = graphene.Float()
+    on_qdrant = graphene.Boolean()
 
     def resolve_count(self, info):
         return embedding.get_tensor_count(self.id)
@@ -175,9 +177,18 @@ def resolve_progress(self, info):
             return 0.0
         else:
             return min(
-                0.1 + (Embedding.resolve_count(self, info) / len(self.project.records) * 0.9), 0.99
+                0.1
+                + (
+                    Embedding.resolve_count(self, info)
+                    / len(self.project.records)
+                    * 0.9
+                ),
+                0.99,
             )
 
+    def resolve_on_qdrant(self, info):
+        return collection_on_qdrant(str(self.project_id), str(self.id))
+
 
 class Project(SQLAlchemyObjectType):
     class Meta:
@@ -667,6 +678,7 @@ class Encoder(graphene.ObjectType):
     applicability = graphene.JSONString()
     platform = graphene.String()
 
+
 class UploadTask(SQLAlchemyObjectType):
     class Meta:
         model = models.UploadTask